# importing the necessary Python libraries and the dataset:

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

data = pd.read_csv("transformed_data.csv")
data2 = pd.read_csv("raw_data.csv")

data

	CODE	COUNTRY	DATE	HDI	TC	TD	STI	POP	GDPCAP
0	AFG	Afghanistan	2019-12-31	0.498	0.000000	0.000000	0.000000	17.477233	7.497754
1	AFG	Afghanistan	2020-01-01	0.498	0.000000	0.000000	0.000000	17.477233	7.497754
2	AFG	Afghanistan	2020-01-02	0.498	0.000000	0.000000	0.000000	17.477233	7.497754
3	AFG	Afghanistan	2020-01-03	0.498	0.000000	0.000000	0.000000	17.477233	7.497754
4	AFG	Afghanistan	2020-01-04	0.498	0.000000	0.000000	0.000000	17.477233	7.497754
...	...	...	...	...	...	...	...	...	...
50413	ZWE	Zimbabwe	2020-10-15	0.535	8.994048	5.442418	4.341855	16.514381	7.549491
50414	ZWE	Zimbabwe	2020-10-16	0.535	8.996528	5.442418	4.341855	16.514381	7.549491
50415	ZWE	Zimbabwe	2020-10-17	0.535	8.999496	5.442418	4.341855	16.514381	7.549491
50416	ZWE	Zimbabwe	2020-10-18	0.535	9.000853	5.442418	4.341855	16.514381	7.549491
50417	ZWE	Zimbabwe	2020-10-19	0.535	9.005405	5.442418	4.341855	16.514381	7.549491

50418 rows × 9 columns

Data Preparation

The dataset that we are using here contains two data files. One file contains raw data, and the other file contains transformed one. But we have to use both datasets for this task, as both of them contain equally important information in different columns. So let’s have a look at both the datasets one by one:

data.head()

	CODE	COUNTRY	DATE	HDI	TC	TD	STI	POP	GDPCAP
0	AFG	Afghanistan	2019-12-31	0.498	0.0	0.0	0.0	17.477233	7.497754
1	AFG	Afghanistan	2020-01-01	0.498	0.0	0.0	0.0	17.477233	7.497754
2	AFG	Afghanistan	2020-01-02	0.498	0.0	0.0	0.0	17.477233	7.497754
3	AFG	Afghanistan	2020-01-03	0.498	0.0	0.0	0.0	17.477233	7.497754
4	AFG	Afghanistan	2020-01-04	0.498	0.0	0.0	0.0	17.477233	7.497754

data2.head()

	iso_code	location	date	total_cases	total_deaths	stringency_index	population	gdp_per_capita	human_development_index	Unnamed: 9	Unnamed: 10	Unnamed: 11	Unnamed: 12	Unnamed: 13
0	AFG	Afghanistan	2019-12-31	0.0	0.0	0.0	38928341	1803.987	0.498	#NUM!	#NUM!	#NUM!	17.477233	7.497754494
1	AFG	Afghanistan	2020-01-01	0.0	0.0	0.0	38928341	1803.987	0.498	#NUM!	#NUM!	#NUM!	17.477233	7.497754494
2	AFG	Afghanistan	2020-01-02	0.0	0.0	0.0	38928341	1803.987	0.498	#NUM!	#NUM!	#NUM!	17.477233	7.497754494
3	AFG	Afghanistan	2020-01-03	0.0	0.0	0.0	38928341	1803.987	0.498	#NUM!	#NUM!	#NUM!	17.477233	7.497754494
4	AFG	Afghanistan	2020-01-04	0.0	0.0	0.0	38928341	1803.987	0.498	#NUM!	#NUM!	#NUM!	17.477233	7.497754494

After having initial impressions of both datasets, I found that we have to combine both datasets by creating a new dataset. But before we create a new dataset, let’s have a look at how many samples of each country are present in the dataset:

data["COUNTRY"].value_counts()

Afghanistan        294
Indonesia          294
Macedonia          294
Luxembourg         294
Lithuania          294
                  ... 
Tajikistan         172
Comoros            171
Lesotho            158
Hong Kong           51
Solomon Islands      4
Name: COUNTRY, Length: 210, dtype: int64

So we don’t have an equal number of samples of each country in the dataset. Let’s have a look at the mode value:

data["COUNTRY"].value_counts().mode()

0    294
Name: COUNTRY, dtype: int64

So 294 is the mode value. We will need to use it for dividing the sum of all the samples related to the human development index, GDP per capita, and the population. Now let’s create a new dataset by combining the necessary columns from both the datasets:

# Aggregating the data

code = data["CODE"].unique().tolist()
country = data["COUNTRY"].unique().tolist()
hdi = []
tc = []
td = []
sti = []
population = data["POP"].unique().tolist()
gdp = []

for i in country:
    hdi.append((data.loc[data["COUNTRY"] == i, "HDI"]).sum()/294)
    tc.append((data2.loc[data2["location"] == i, "total_cases"]).sum())
    td.append((data2.loc[data2["location"] == i, "total_deaths"]).sum())
    sti.append((data.loc[data["COUNTRY"] == i, "STI"]).sum()/294)
    population.append((data2.loc[data2["location"] == i, "population"]).sum()/294)

aggregated_data = pd.DataFrame(list(zip(code, country, hdi, tc, td, sti, population)), 
                               columns = ["Country Code", "Country", "HDI", 
                                          "Total Cases", "Total Deaths", 
                                          "Stringency Index", "Population"])

aggregated_data.head()

	Country Code	Country	HDI	Total Cases	Total Deaths	Stringency Index	Population
0	AFG	Afghanistan	0.498000	5126433.0	165875.0	3.049673	17.477233
1	ALB	Albania	0.600765	1071951.0	31056.0	3.005624	14.872537
2	DZA	Algeria	0.754000	4893999.0	206429.0	3.195168	17.596309
3	AND	Andorra	0.659551	223576.0	9850.0	2.677654	11.254996
4	AGO	Angola	0.418952	304005.0	11820.0	2.965560	17.307957

I have not included the GDP per capita column yet. I didn’t find the correct figures for GDP per capita in the dataset. So it will be better to manually collect the data about the GDP per capita of the countries.

As we have so many countries in this data, it will not be easy to manually collect the data about the GDP per capita of all the countries. So let’s select a subsample from this dataset. To create a subsample from this dataset, I will be selecting the top 10 countries with the highest number of covid-19 cases. It will be a perfect sample to study the economic impacts of covid-19. So let’s sort the data according to the total cases of Covid-19:

# Sorting Data According to Total Cases

data = aggregated_data.sort_values(by=["Total Cases"], ascending=False)
data.head()

	Country Code	Country	HDI	Total Cases	Total Deaths	Stringency Index	Population
200	USA	United States	0.92400	746014098.0	26477574.0	3.350949	19.617637
27	BRA	Brazil	0.75900	425704517.0	14340567.0	3.136028	19.174732
90	IND	India	0.64000	407771615.0	7247327.0	3.610552	21.045353
157	RUS	Russia	0.81600	132888951.0	2131571.0	3.380088	18.798668
150	PER	Peru	0.59949	74882695.0	3020038.0	3.430126	17.311165

Now here’s how we can select the top 10 countries with the highest number of cases:

# Top 10 Countries with Highest Covid Cases

data = data.head(10)
data

	Country Code	Country	HDI	Total Cases	Total Deaths	Stringency Index	Population
200	USA	United States	0.924000	746014098.0	26477574.0	3.350949	19.617637
27	BRA	Brazil	0.759000	425704517.0	14340567.0	3.136028	19.174732
90	IND	India	0.640000	407771615.0	7247327.0	3.610552	21.045353
157	RUS	Russia	0.816000	132888951.0	2131571.0	3.380088	18.798668
150	PER	Peru	0.599490	74882695.0	3020038.0	3.430126	17.311165
125	MEX	Mexico	0.774000	74347548.0	7295850.0	3.019289	18.674802
178	ESP	Spain	0.887969	73717676.0	5510624.0	3.393922	17.660427
175	ZAF	South Africa	0.608653	63027659.0	1357682.0	3.364333	17.898266
42	COL	Colombia	0.581847	60543682.0	1936134.0	3.357923	17.745037
199	GBR	United Kingdom	0.922000	59475032.0	7249573.0	3.353883	18.033340

Now I will add two more columns (GDP per capita before Covid-19, GDP per capita during Covid-19) to this dataset:

data["GDP Before Covid"] = [65279.53, 8897.49, 2100.75, 
                            11497.65, 7027.61, 9946.03, 
                            29564.74, 6001.40, 6424.98, 42354.41]
data["GDP During Covid"] = [63543.58, 6796.84, 1900.71, 
                            10126.72, 6126.87, 8346.70, 
                            27057.16, 5090.72, 5332.77, 40284.64]
data

	Country Code	Country	HDI	Total Cases	Total Deaths	Stringency Index	Population	GDP Before Covid	GDP During Covid
200	USA	United States	0.924000	746014098.0	26477574.0	3.350949	19.617637	65279.53	63543.58
27	BRA	Brazil	0.759000	425704517.0	14340567.0	3.136028	19.174732	8897.49	6796.84
90	IND	India	0.640000	407771615.0	7247327.0	3.610552	21.045353	2100.75	1900.71
157	RUS	Russia	0.816000	132888951.0	2131571.0	3.380088	18.798668	11497.65	10126.72
150	PER	Peru	0.599490	74882695.0	3020038.0	3.430126	17.311165	7027.61	6126.87
125	MEX	Mexico	0.774000	74347548.0	7295850.0	3.019289	18.674802	9946.03	8346.70
178	ESP	Spain	0.887969	73717676.0	5510624.0	3.393922	17.660427	29564.74	27057.16
175	ZAF	South Africa	0.608653	63027659.0	1357682.0	3.364333	17.898266	6001.40	5090.72
42	COL	Colombia	0.581847	60543682.0	1936134.0	3.357923	17.745037	6424.98	5332.77
199	GBR	United Kingdom	0.922000	59475032.0	7249573.0	3.353883	18.033340	42354.41	40284.64

Analyzing the Spread of Covid-19

Now let’s start by analyzing the spread of covid-19 in all the countries with the highest number of covid-19 cases. I will first have a look at all the countries with the highest number of covid-19 cases:

figure = px.bar(data, y='Total Cases', x='Country',
            title="Countries with Highest Covid Cases")
figure.show()

We can see that the USA is comparatively having a very high number of covid-19 cases as compared to Brazil and India in the second and third positions. Now let’s have a look at the total number of deaths among the countries with the highest number of covid-19 cases:

figure = px.bar(data, y='Total Deaths', x='Country',
            title="Countries with Highest Deaths")
figure.show()

Just like the total number of covid-19 cases, the USA is leading in the deaths, with Brazil and India in the second and third positions. One thing to notice here is that the death rate in India, Russia, and South Africa is comparatively low according to the total number of cases. Now let’s compare the total number of cases and total deaths in all these countries:

fig = go.Figure()
fig.add_trace(go.Bar(
    x=data["Country"],
    y=data["Total Cases"],
    name='Total Cases',
    marker_color='indianred'
))
fig.add_trace(go.Bar(
    x=data["Country"],
    y=data["Total Deaths"],
    name='Total Deaths',
    marker_color='lightsalmon'
))
fig.update_layout(barmode='group', xaxis_tickangle=-45)
fig.show()

Now let’s have a look at the percentage of total deaths and total cases among all the countries with the highest number of covid-19 cases:

# Percentage of Total Cases and Deaths
cases = data["Total Cases"].sum()
deceased = data["Total Deaths"].sum()

labels = ["Total Cases", "Total Deaths"]
values = [cases, deceased]

fig = px.pie(data, values=values, names=labels, 
             title='Percentage of Total Cases and Deaths', hole=0.5)
fig.show()

Below is how you can calculate the death rate of Covid-19 cases:

death_rate = (data["Total Deaths"].sum() / data["Total Cases"].sum()) * 100
print("Death Rate = ", death_rate)

Death Rate =  3.6144212045653767

Another important column in this dataset is the stringency index. It is a composite measure of response indicators, including school closures, workplace closures, and travel bans. It shows how strictly countries are following these measures to control the spread of covid-19:

fig = px.bar(data, x='Country', y='Total Cases',
             hover_data=['Population', 'Total Deaths'], 
             color='Stringency Index', height=400, 
             title= "Stringency Index during Covid-19")
fig.show()

Here we can see that India is performing well in the stringency index during the outbreak of covid-19.

Analyzing Covid-19 Impacts on Economy

Now let’s move to analyze the impacts of covid-19 on the economy. Here the GDP per capita is the primary factor for analyzing the economic slowdowns caused due to the outbreak of covid-19. Let’s have a look at the GDP per capita before the outbreak of covid-19 among the countries with the highest number of covid-19 cases:

fig = px.bar(data, x='Country', y='Total Cases',
             hover_data=['Population', 'Total Deaths'], 
             color='GDP Before Covid', height=400, 
             title="GDP Per Capita Before Covid-19")
fig.show()

Now let’s have a look at the GDP per capita during the rise in the cases of covid-19:

fig = px.bar(data, x='Country', y='Total Cases',
             hover_data=['Population', 'Total Deaths'], 
             color='GDP During Covid', height=400, 
             title="GDP Per Capita During Covid-19")
fig.show()

Now let’s compare the GDP per capita before covid-19 and during covid-19 to have a look at the impact of covid-19 on the GDP per capita:

fig = go.Figure()
fig.add_trace(go.Bar(
    x=data["Country"],
    y=data["GDP Before Covid"],
    name='GDP Per Capita Before Covid-19',
    marker_color='indianred'
))
fig.add_trace(go.Bar(
    x=data["Country"],
    y=data["GDP During Covid"],
    name='GDP Per Capita During Covid-19',
    marker_color='lightsalmon'
))
fig.update_layout(barmode='group', xaxis_tickangle=-45)
fig.show()

You can see a drop in GDP per capita in all the countries with the highest number of covid-19 cases.

One other important economic factor is Human Development Index. It is a statistic composite index of life expectancy, education, and per capita indicators. Let’s have a look at how many countries were spending their budget on the human development:

fig = px.bar(data, x='Country', y='Total Cases',
             hover_data=['Population', 'Total Deaths'], 
             color='HDI', height=400, 
             title="Human Development Index during Covid-19")
fig.show()

So this is how we can analyze the spread of Covid-19 and its impact on the economy.

Conclusion

In this task, we studied the spread of covid-19 among the countries and its impact on the global economy. We saw that the outbreak of covid-19 resulted in the highest number of covid-19 cases and deaths in the united states. One major reason behind this is the stringency index of the United States. It is comparatively low according to the population. We also analyzed how the GDP per capita of every country was affected during the outbreak of covid-19. I hope you liked this article on Covid-19 impacts analysis using Python. Feel free to ask valuable questions in the comments section below.